Digital images are currently being captured by many different types of devices and there is a desire to display those images on a wide variety of electronic displays and printed outputs of many different sizes. Given the proliferation of image capture by digital photography and the variety of display types, such as mobile phones, Personal Digital Assistants, printers, and the like, image resizing occurs frequently. Historically, cropping and scaling, i.e., image down-sampling, have been used to shrink images, and scaling to achieve image upsampling has been used to enlarge images. Cropping is generally effective for shrinking images where there is only one region of interest in the image. Scaling by down-sampling works reasonably well for shrinking images containing low frequency information. However, scaling is able to introduce unwanted distortions because the scaling, and hence loss of image information, is applied uniformly to the image. With proper region identification, cropping may be more effective than naïve scaling in applications such as the generation of thumbnail images because the resultant images are more recognizable. However, naïve cropping may result in the cropping away of contextual information that is important to the viewer. To address the above described deficiencies of image resizing, content-aware resizing is used. The development of content-aware resizing techniques includes developing automated methods that can resize images by preserving the visual importance of image content.
Seam Carving is one content-aware, intelligent image resizing technique used to modify images to more effectively display that image at a different, such as a reduced, size. Seam carving techniques operate by identifying a connected path, or “seam” of pixels within an image that is characterized as having low importance or information content. The importance or image contribution of a particular pixel relative to its neighboring pixels is referred to herein as “entropy.” Image resizing techniques that operate on color images to identify low entropy pixels operate on the luminance channel of the image. Such techniques are able to introduce distortions into image sections that are close to iso-luminant but that have different colors, since information from the chrominance channels is not considered in determining pixel entropy.
Accordingly, what is needed in this art are increasingly sophisticated systems and methods for determining color pixel entropy to support content aware, intelligent image resizing.
The following U.S. Patents, U.S. Patent Applications, and Publications are incorporated herein in their entirety by reference.    U.S. patent application Ser. No. 12/330,879, filed Dec. 9, 2008.    “Seam Carving for Content-Aware Image Resizing”, by: Shai Avidan and Ariel Shamir, ACM Transactions on Graphics, Vol. 26, No. 3 (2007).    “A Note On The Gradient Of A Multi-Image”, S. DiZenzo, Computer Vision, Graphics and Image Processing, 33:116-125 (1986).    “Digital Color Imaging Handbook”, 1st Ed., CRC Press (2003), ISBN-13: 97808-4930-9007.    “Control of Color Imaging Systems: Analysis and Design”, CRC Press (2009), ISBN-13: 97808-4933-7468.